AI-Driven Personalization of End-of-Life Care for the Elderly

NCT07027618 | Not Yet Recruiting DMC

• 1 other identifier: BUMS
• Study Type: interventional
• Target: 140
• Locations: 0 countries
• Sites: N/A

Brief Summary

This study aims to evaluate the effectiveness of an artificial intelligence (AI)-based software in personalizing high-quality end-of-life care for elderly patients. As the elderly population grows, providing tailored and quality care during the final stages of life becomes increasingly important. This AI software continuously monitors vital signs and behaviors through wearable sensors, offers smart medication reminders, alerts the care team to potential risks, and provides personalized care plans along with psychological and social support. The study is designed as a randomized controlled trial comparing two groups: one receiving standard end-of-life care and the other using the AI software. Key outcomes include improving quality of life, reducing adverse events like falls and emergency hospitalizations, increasing patient and family satisfaction, improving medication management, and reducing caregiver burden. Data will be collected over six months to assess these effects. The results will help determine whether AI technology can enhance end-of-life care for seniors and support families and healthcare providers.

Conditions

Artificial Intelligence (AI); Dementia; Palliative Care

Outcome Measures

Primary Outcomes (1)

• Change in Quality of Life Score

  Assessed using a validated instrument such as the Quality of Life at the End of Life (QUAL-E) scale to measure overall well-being, comfort, and satisfaction with care during end-of-life.

  Up to 3 months after intervention start

Study Arms (2)

AI-based Personalized End-of-Life Care

EXPERIMENTAL

Participants in this group will receive personalized end-of-life care supported by an AI-based software. The software monitors vital signs and behavior through wearable sensors, provides intelligent medication reminders, issues preventive alerts to the care team, delivers personalized care plans, and offers psychological and social support through communication features.

Behavioral: AI-based Software for Personalized End-of-Life Care

Standard End-of-Life Care

ACTIVE COMPARATOR

Participants in this group will receive standard end-of-life care without the use of the AI-based software. Care is provided according to usual clinical practices and guidelines.

Behavioral: AI-based Software for Personalized End-of-Life Care

Interventions

AI-based Software for Personalized End-of-Life Care BEHAVIORAL

This intervention involves the use of an AI-powered software system designed to personalize end-of-life care for elderly patients. The software continuously monitors vital signs and behavior through wearable sensors, sends smart medication reminders, issues preventive alerts to care providers, delivers customized care plans, and provides psychological and social support through communication features.

AI-based Personalized End-of-Life Care; Standard End-of-Life Care

Eligibility Criteria

• Age: 60 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Age ≥ 60 years
• Clinical diagnosis of being in the end-of-life stage, based on criteria such as the Karnofsky Performance Scale or Palliative Performance Scale
• Informed consent obtained from the participant or legal representative
• Ability to use technology independently or with support provided by the research team
• Access to necessary equipment for the intervention (e.g., wearable sensors, smartphone/tablet)

You may not qualify if:

• Presence of severe cognitive impairment preventing software use
• Voluntary withdrawal from the study at any stage
• Critical medical deterioration or death during the study
• Poor adherence or insufficient engagement with the intervention software in the intervention group

Sponsors & Collaborators

• Baqiyatallah Medical Sciences University: lead

MeSH Terms

Conditions

Dementia

Condition Hierarchy (Ancestors)

Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Neurocognitive Disorders; Mental Disorders

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Purpose: PREVENTION
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Model Details: Participants will be randomly assigned to one of two parallel groups: an intervention group receiving AI-based palliative care through a personalized software system, and a control group receiving standard end-of-life care without the AI intervention. Randomization will be conducted using block randomization to ensure balance in baseline characteristics. Although the study is open-label, outcome assessors will be blinded to group assignments to minimize assessment bias.

Study Record Dates

• First Submitted: June 11, 2025
• First Posted: June 18, 2025
• Study Start: July 1, 2025
• Primary Completion (Estimated): August 1, 2026
• Study Completion (Estimated): September 1, 2026
• Last Updated: June 18, 2025

Record last verified: 2025-06